Abstract:
Healthcare 5.0 focuses on a personalized patient-centric approach, and combines advanced technologies like artificial intelligence (AI), blockchain, Internet-of-Things (IoT), and Big data to form preventive, proactive, and emotive healthcare. To assure privacy of electronic health records (EHRs) in Healthcare 5.0, blockchain has emerged as a disruptive technology owing to its properties of assured immutability, chronology, and transparent nature. Recent research has integrated blockchain technology with deep learning (DL) models to enhance the predictive capabilities for future disease occurrences. Nonetheless, DL models often necessitate a substantial volume of labeled data, a resource that may not be readily available in all scenarios. Thus, boosting mechanisms can overcome this limitation by leveraging small labelled datasets and improve the model generalization capability. Motivated by this, we propose a scheme, Bl-Boost, which combines extreme gradient boosting (XG) with long short term memory (LSTM) model for making accurate predictions on EHR data. We store the model predictions on a local interplanetary file systems (IPFS) server, and hash information is published in main blockchain. Via smart contracts (SCs), we for privacy-preserved access control on the data. The experimental validation is performed on the benchmark heart failure prediction dataset in terms of accuracy, loss, and precision matrix for LSTM and XG-Boost LSTM models. We present sample contracts for data sharing, and for blockchain metrics, we validate our performance of scalability, IPFS cost, and trust probability against collusion attacks. The proposed outcomes indicate that the scheme has strong potential for viability in real-world deployment scenarios.
